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options(scipen = 999)

library(tidyverse)
library(ggplot2)
library(plotly)
library(data.table)
library(lubridate)
library(dplyr)
library(corrplot)
library(leaflet)

#Get only the Summary data for some initial analysis

summary_raw_data<-read_csv ('data/claim_summary_v1.csv' )

Parsed with column specification:
cols(
  .default = col_double(),
  esco_id = col_character(),
  bene_hic_num = col_character(),
  claim_first_dialysis_date = col_character(),
  claim_last_dialysis_date = col_character()
)
See spec(...) for full column specifications.

summary_wds<-summary_raw_data

#Get the data with all the required fields for analysis
#claim_detail_raw_data<-fread("data/claim_details.csv",sep = "|",fill = T)

#detail_raw_data<-read_csv ('data/claim_details.csv' )

locations<-read_csv('data/ESCo_LOCATIONS_lo.csv' )

Parsed with column specification:
cols(
  location_id = col_character(),
  short_name = col_character(),
  location_address_1 = col_character(),
  location_address_2 = col_character(),
  location_city = col_character(),
  location_state = col_character(),
  location_zip_code = col_double(),
  latitude = col_double(),
  longitude = col_double()
)

INITIAL ANALYSIS WITH THE SUMMARY DATA ONLY.

summary_wds %>% 
  select (-esco_id,-bene_hic_num,-esco_aligned_flag) %>% 
  filter(patient_id == '811454') %>% 
  arrange(dos_year,dos_month)

NA

str(summary_wds)

Classes ‘spec_tbl_df’, ‘tbl_df’, ‘tbl’ and 'data.frame':    10364 obs. of  35 variables:
 $ esco_id                  : chr  "E0050" "E0050" "E0050" "E0050" ...
 $ bene_hic_num             : chr  "161446232A" "161446232A" "161446232A" "161480276A" ...
 $ patient_id               : num  811454 811454 811454 814249 814249 ...
 $ dos_year                 : num  2018 2018 2018 2017 2017 ...
 $ dos_month                : num  1 8 11 2 5 1 4 4 2 8 ...
 $ claim_first_dialysis_date: chr  "2018-01-03 00:00:00.000" "2018-08-01 00:00:00.000" "2018-11-02 00:00:00.000" "2017-02-01 00:00:00.000" ...
 $ claim_last_dialysis_date : chr  "2018-01-31 00:00:00.000" "2018-08-31 00:00:00.000" "2018-11-30 00:00:00.000" "2017-02-06 00:00:00.000" ...
 $ dci_claims               : num  1 0 0 1 1 1 1 1 1 0 ...
 $ non_dci_claims           : num  3 7 11 3 1 1 6 67 1 3 ...
 $ payment                  : num  2512 2853 3498 2272 2617 ...
 $ part_a                   : num  2053 2323 2160 2068 2432 ...
 $ part_b_phys              : num  459 530 1300 204 185 ...
 $ part_b_dme               : num  0 0 39.1 0 0 ...
 $ inpatient                : num  0 0 0 0 0 ...
 $ outpatient_dialysis      : num  2053 2323 2160 2068 2432 ...
 $ outpatient_er            : num  0 0 0 0 0 ...
 $ outpatient_other         : num  0 0 0 0 0 ...
 $ hha                      : num  0 0 0 0 0 0 0 0 0 0 ...
 $ snf                      : num  0 0 0 0 0 ...
 $ hospice                  : num  0 0 0 0 0 0 0 0 0 0 ...
 $ full_encounter           : num  0 0 0 0 0 0 0 0 0 0 ...
 $ phys_neph                : num  222 222 222 185 185 ...
 $ phys_hosp                : num  0 0 0 0 0 ...
 $ phys_ed                  : num  0 0 0 0 0 ...
 $ vasc_access              : num  0 0 916 0 0 ...
 $ ambulance                : num  0 0 0 0 0 ...
 $ phys_other               : num  236.9 307.9 162.1 19.2 0 ...
 $ dme                      : num  0 0 39.1 0 0 ...
 $ esco_aligned_flag        : num  1 1 1 1 1 1 1 1 1 1 ...
 $ inpatient_fluid          : num  0 0 0 0 0 0 0 0 0 0 ...
 $ outpatient_er_fluid      : num  0 0 0 0 0 0 0 0 0 0 ...
 $ part_a_other_fluid       : num  0 0 0 0 0 0 0 0 0 0 ...
 $ inpatient_access         : num  0 0 0 0 0 0 0 0 0 0 ...
 $ outpatient_er_access     : num  0 0 0 0 0 0 0 0 0 0 ...
 $ part_a_other_access      : num  0 0 0 0 0 0 0 0 0 0 ...
 - attr(*, "spec")=
  .. cols(
  ..   esco_id = col_character(),
  ..   bene_hic_num = col_character(),
  ..   patient_id = col_double(),
  ..   dos_year = col_double(),
  ..   dos_month = col_double(),
  ..   claim_first_dialysis_date = col_character(),
  ..   claim_last_dialysis_date = col_character(),
  ..   dci_claims = col_double(),
  ..   non_dci_claims = col_double(),
  ..   payment = col_double(),
  ..   part_a = col_double(),
  ..   part_b_phys = col_double(),
  ..   part_b_dme = col_double(),
  ..   inpatient = col_double(),
  ..   outpatient_dialysis = col_double(),
  ..   outpatient_er = col_double(),
  ..   outpatient_other = col_double(),
  ..   hha = col_double(),
  ..   snf = col_double(),
  ..   hospice = col_double(),
  ..   full_encounter = col_double(),
  ..   phys_neph = col_double(),
  ..   phys_hosp = col_double(),
  ..   phys_ed = col_double(),
  ..   vasc_access = col_double(),
  ..   ambulance = col_double(),
  ..   phys_other = col_double(),
  ..   dme = col_double(),
  ..   esco_aligned_flag = col_double(),
  ..   inpatient_fluid = col_double(),
  ..   outpatient_er_fluid = col_double(),
  ..   part_a_other_fluid = col_double(),
  ..   inpatient_access = col_double(),
  ..   outpatient_er_access = col_double(),
  ..   part_a_other_access = col_double()
  .. )

summary(summary_wds)

   esco_id          bene_hic_num         patient_id        dos_year      dos_month      claim_first_dialysis_date
 Length:10364       Length:10364       Min.   : 30111   Min.   :2017   Min.   : 1.000   Length:10364             
 Class :character   Class :character   1st Qu.:798197   1st Qu.:2017   1st Qu.: 4.000   Class :character         
 Mode  :character   Mode  :character   Median :806827   Median :2018   Median : 7.000   Mode  :character         
                                       Mean   :711615   Mean   :2018   Mean   : 6.521                            
                                       3rd Qu.:812964   3rd Qu.:2018   3rd Qu.: 9.000                            
                                       Max.   :910902   Max.   :2018   Max.   :12.000                            
 claim_last_dialysis_date   dci_claims     non_dci_claims     payment              part_a           part_b_phys     
 Length:10364             Min.   :0.0000   Min.   :  0.0   Min.   :    34.18   Min.   :    13.17   Min.   :    0.0  
 Class :character         1st Qu.:1.0000   1st Qu.:  3.0   1st Qu.:  2664.22   1st Qu.:  2321.80   1st Qu.:  224.9  
 Mode  :character         Median :1.0000   Median :  6.0   Median :  3284.65   Median :  2730.59   Median :  369.4  
                          Mean   :0.9818   Mean   : 11.7   Mean   :  6916.75   Mean   :  5898.76   Mean   :  957.7  
                          3rd Qu.:1.0000   3rd Qu.: 14.0   3rd Qu.:  6039.45   3rd Qu.:  4754.09   3rd Qu.: 1002.8  
                          Max.   :5.0000   Max.   :138.0   Max.   :139204.35   Max.   :131619.23   Max.   :22145.9  
   part_b_dme         inpatient      outpatient_dialysis outpatient_er     outpatient_other       hha              snf         
 Min.   :    0.00   Min.   :     0   Min.   :   0        Min.   :    0.0   Min.   :    0.0   Min.   :   0.0   Min.   :    0.0  
 1st Qu.:    0.00   1st Qu.:     0   1st Qu.:2037        1st Qu.:    0.0   1st Qu.:    0.0   1st Qu.:   0.0   1st Qu.:    0.0  
 Median :    0.00   Median :     0   Median :2323        Median :    0.0   Median :    0.0   Median :   0.0   Median :    0.0  
 Mean   :   60.23   Mean   :  2601   Mean   :2299        Mean   :  106.7   Mean   :  391.9   Mean   : 160.5   Mean   :  323.2  
 3rd Qu.:    4.33   3rd Qu.:     0   3rd Qu.:2609        3rd Qu.:    0.0   3rd Qu.:  115.2   3rd Qu.:   0.0   3rd Qu.:    0.0  
 Max.   :17581.02   Max.   :131619   Max.   :7283        Max.   :26135.5   Max.   :25749.6   Max.   :9011.7   Max.   :22185.7  
    hospice        full_encounter   phys_neph       phys_hosp          phys_ed         vasc_access     ambulance     
 Min.   :   0.00   Min.   :0      Min.   :  0.0   Min.   :    0.0   Min.   :   0.00   Min.   :   0   Min.   :   0.0  
 1st Qu.:   0.00   1st Qu.:0      1st Qu.:180.8   1st Qu.:    0.0   1st Qu.:   0.00   1st Qu.:   0   1st Qu.:   0.0  
 Median :   0.00   Median :0      Median :213.8   Median :    0.0   Median :   0.00   Median :   0   Median :   0.0  
 Mean   :  16.37   Mean   :0      Mean   :189.0   Mean   :  259.2   Mean   :  28.58   Mean   :  75   Mean   : 100.7  
 3rd Qu.:   0.00   3rd Qu.:0      3rd Qu.:220.4   3rd Qu.:    0.0   3rd Qu.:   0.00   3rd Qu.:   0   3rd Qu.:   0.0  
 Max.   :7032.21   Max.   :0      Max.   :877.3   Max.   :10036.4   Max.   :1007.74   Max.   :5523   Max.   :8815.2  
   phys_other            dme           esco_aligned_flag inpatient_fluid   outpatient_er_fluid part_a_other_fluid inpatient_access
 Min.   :    0.00   Min.   :    0.00   Min.   :1         Min.   :    0.0   Min.   :   0.000    Min.   :    0.00   Min.   :    0   
 1st Qu.:   17.26   1st Qu.:    0.00   1st Qu.:1         1st Qu.:    0.0   1st Qu.:   0.000    1st Qu.:    0.00   1st Qu.:    0   
 Median :  111.42   Median :    0.00   Median :1         Median :    0.0   Median :   0.000    Median :    0.00   Median :    0   
 Mean   :  305.26   Mean   :   60.23   Mean   :1         Mean   :  197.4   Mean   :   7.179    Mean   :   36.26   Mean   :  281   
 3rd Qu.:  309.44   3rd Qu.:    4.33   3rd Qu.:1         3rd Qu.:    0.0   3rd Qu.:   0.000    3rd Qu.:    0.00   3rd Qu.:    0   
 Max.   :16197.08   Max.   :17581.02   Max.   :1         Max.   :42387.0   Max.   :4956.360    Max.   :14684.18   Max.   :92109   
 outpatient_er_access part_a_other_access
 Min.   :    0.00     Min.   :    0.00   
 1st Qu.:    0.00     1st Qu.:    0.00   
 Median :    0.00     Median :    0.00   
 Mean   :   14.89     Mean   :   92.67   
 3rd Qu.:    0.00     3rd Qu.:    0.00   
 Max.   :11694.69     Max.   :15409.49   

summary(summary_wds$payment)

     Min.   1st Qu.    Median      Mean   3rd Qu.      Max. 
    34.18   2664.22   3284.65   6916.75   6039.45 139204.35 

summary_wds %>% 
  ggplot(aes(x=payment)) +
  geom_histogram(breaks = seq(500,100000,by=1000),
                 bins=20,
                 col="red",
                 fill = "green",
                 alpha = 0.2) +
 scale_x_log10()  +
  labs(x="Payments", y = "Counts",title = "Payments Histogram")  

change_increase<-summary_wds %>% 
  select (patient_id,payment,dos_year,dos_month) %>% 
  group_by(patient_id,dos_year) %>% 
  summarise(sum_payment = sum(payment), num_of_months = NROW(dos_month)) %>% 
  ungroup %>% 
  filter(num_of_months >11) %>% 
  arrange (num_of_months,patient_id,dos_year,desc(sum_payment)) 


change_increase$patient_id <- as.factor(change_increase$patient_id)

 change_increase %>% 
  filter (patient_id == '403675')

change_increase$patient_id <- as.factor(change_increase$patient_id )
change_increase$dos_year <- as.factor(change_increase$dos_year)

pl <- ggplot( change_increase,aes(y=sum_payment, x = patient_id ,fill=dos_year)) +
  geom_bar(stat = "identity",position = 'dodge') +
  labs(x="patient_id", y= "Payments") +
  ggtitle("yearly payments difference for patients")
       
pl

#pivot the data and understand hte percentage change in payments and get expensive patients
expensive_patients<-pivot_wider(change_increase,
                                names_from = dos_year,
                                values_from = sum_payment, 
                                values_fill = list(sum_payment = 0)) %>% 
          mutate(percent_change = (`2018`-`2017`)/`2017` * 100) %>% 
          filter(percent_change > 50 & `2017` != 0) %>% 
          arrange (desc(percent_change))

expensive_patients

summary_wds %>% 
  filter(patient_id =='798094') %>% 
  arrange (dos_year,dos_month)

NA

year_plot<- summary_wds %>% 
  group_by(dos_year,dos_month) %>% 
  summarise(sum_pay = sum(payment)) %>% 
  ungroup()  
year_plot

year_plot$dos_month <- as.factor(year_plot$dos_month)
year_plot$dos_year <- as.factor(year_plot$dos_year)

pl <- ggplot( year_plot,aes(y=sum_pay, x = dos_month ,fill=dos_year)) +
  geom_bar(stat = "identity",position = 'dodge') +
  labs(x="Months", y= "Payments") +
  ggtitle("payments increase every year")
       
pl

year_plot

NA

#redo this with non summarize raw data

plbox <- ggplot(year_plot,aes(y=sum_pay, x = dos_month) )+
  geom_boxplot() +
  labs(x="Months", y= "Payments") +
  ggtitle("T")
       
plbox

NA

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summary_2017<-summary_wds %>% 
  group_by(dos_month,dos_year) %>% 
  summarise(
            total_patients = NROW(patient_id),
            totalpayments = sum(payment),
            min_pay = min(payment),
            max_pay = max(payment),
            avg_pay = sum(payment)/NROW(patient_id)) %>% 
  ungroup() %>%
  arrange(dos_year,dos_month) %>% 
  filter(dos_year =="2017")

summary_2017

summary_2017_pivot <- summary_2017 %>% 
                      pivot_longer(min_pay:avg_pay, names_to = "payment_type", values_to = "payment")

summary_2017_pivot$dos_month = as.factor(summary_2017_pivot$dos_month)

summary_2017_pivot

NA

 
pl<- ggplot(summary_2017_pivot,aes(x=dos_month, y = payment, fill = payment_type)) +
      geom_col(stat = "identity",position = 'dodge') +
      geom_hline(yintercept = 6000, linetype = "dashed",color = "darkred") +
      labs(x="Months", y = "Payments") +
      ggtitle("2017 minimum, maximum and average payments")

Ignoring unknown parameters: stat

pl + scale_y_continuous(breaks = seq(0,130000,5000))

NA
NA

all_payments<-summary_wds %>% 
  group_by(dos_month) %>% 
  summarise(
            total_patients = NROW(patient_id),
            totalpayments = sum(payment),
            min_pay = min(payment),
            max_pay = max(payment),
            avg_pay = sum(payment)/NROW(patient_id)) %>% 
  ungroup() %>% 
  pivot_longer(min_pay:avg_pay, names_to='payment_type',values_to = 'payments')

all_payments

#redo  with box plot

pl_all<- ggplot(all_payments,aes(x=dos_month, y = payments, fill = payment_type)) +
      geom_col(stat = "identity",position = 'dodge') +
      geom_hline(yintercept = 6000, linetype = "dashed",color = "darkred") +
      labs(x="Months", y = "Payments") +
      ggtitle("minimum, maximum and average payments")

Ignoring unknown parameters: stat

pl_all + scale_y_continuous(breaks = seq(0,130000,5000))

NA
NA

ANALYSIS USING SUMMARY AND DETAIL DATA Get the data and clean it

#get detail and summary data and save it in a df
detail_raw_data<-read_csv ('data/claim_details.csv' )

Duplicated column names deduplicated: 'patient_id' => 'patient_id_1' [37]Parsed with column specification:
cols(
  .default = col_double(),
  esco_id = col_character(),
  bene_hic_num = col_character(),
  claim_first_dialysis_date = col_character(),
  claim_last_dialysis_date = col_character(),
  location_id = col_character(),
  start_date = col_datetime(format = ""),
  end_date = col_datetime(format = ""),
  esrd_date = col_datetime(format = ""),
  modality = col_character(),
  hgb_cutoff = col_character(),
  epo_ceiling = col_character(),
  route_freq = col_character(),
  drug_name = col_character(),
  ferritin_cutoff = col_character(),
  tsat_cutoff = col_character(),
  sup_name = col_character(),
  tx_epo = col_character(),
  venofer_given = col_character(),
  venofer_wasted = col_character(),
  ferrlecit_given = col_character()
  # ... with 42 more columns
)
See spec(...) for full column specifications.
1 parsing failure.
 row       col   expected actual                     file
7272 esrd_date date like    NULL 'data/claim_details.csv'

dci_data<-detail_raw_data

dci_data<-dci_data %>%
select(-esco_id,-bene_hic_num,-full_encounter,-phys_neph,-phys_hosp,-phys_ed,-ambulance,-phys_other,-esco_aligned_flag,-inpatient_fluid,-outpatient_er_fluid,-part_a_other_fluid,-patient_id_1,-dos_yyyy,-dos_mm,-esrd_date,-hgb_date,-tsat_date,-ferr_date,-albumin_date,-pth_date,-ca_date,-cca_date,-ph_date,-k_date,-urr_date,-ktv_date,-epo_given,-venofer_wasted,-inpatient_access,-outpatient_er_access,-part_a_other_access,-dci_claims,-non_dci_claims,-hha,-hospice,-dme,-start_date,-end_date,-epo_ceiling,-route_freq,-drug_name,-ferritin_cutoff,-tsat_cutoff,-hgb_cutoff,-ferrlecit_wasted,-zemplar_iv_wasted,-calcijex_iv_wasted,-feraheme_wasted,-hectorol_iv_wasted,-tx_missed,-sensipar_dispensed)

#make sure all the columns are in correct data types.

#change claims date columns to date datatype
date_columns <- c("claim_first_dialysis_date","claim_last_dialysis_date")
dci_data[date_columns] <- lapply(dci_data[date_columns],as.Date)

#change the other columns to factor
fac_columns <- c("patient_id","dos_year","location_id","dos_month","modality","tx","tx_epo","sup_name")
dci_data[fac_columns] <- lapply(dci_data[fac_columns],as.factor)

#change the below columns to logical
bool_columns <- c("epo_protocol_flag","iron_protocol_flag","nutsup_protocol_flag","hgb_exclude_flag","active_flag")
dci_data[bool_columns] <- lapply(dci_data[bool_columns],as.logical)
 
#Make below colums as logical true if they have any value else if they have null make it logical false.
dci_data<-dci_data %>% 
  mutate_at(vars("ferrlecit_given","feraheme_given","venofer_given","zemplar_iv_given","hectorol_iv_given","calcijex_iv_given","zemplar_or_given","hectorol_or_given","calcijex_iv_given","zemplar_or_given","hectorol_or_given","calcijex_or_given","activase_given","prostat_given","nepro_given","liquacel_given","has_catheter","aranesp_given","protinex_given","mircera_given","sensipar_given","parsabiv_given","protein_bar_given",
                 "ice_cream_given","gelatein_given"),
            funs(case_when(.=="NULL" ~ FALSE,
                                 TRUE ~ TRUE)))

#FIRST make the null VALUES in char col to zero's except for date columns

num_columns <- c("hgb","tsat","ferr","albumin","pth","ca","cca","ph","k","urr","ktv","tx_epo")
dci_data[num_columns]<-dci_data[num_columns]%>% 
                            replace(.=="NULL","0") 

#change all the character columns ot numeric
dci_data<-dci_data %>% mutate_if(is.character,as.numeric)
 
#names(dci_data)
 
#as for date columns we cannot replace na values ot 0. first make them character columns and then make rest to the na values to 0 in entire dataframe
dci_data$claim_first_dialysis_date <-as.character.Date(dci_data$claim_first_dialysis_date )
dci_data$claim_last_dialysis_date <-as.character.Date(dci_data$claim_last_dialysis_date )
 
#make all na values to 0 in entire dataframe
 dci_data[is.na(dci_data)]<-0
# sum(is.na(dci_data))

 #making the dates column back to date datatype
dci_data$claim_first_dialysis_date <- as.Date(dci_data$claim_first_dialysis_date)
dci_data$claim_last_dialysis_date <- as.Date(dci_data$claim_last_dialysis_date)

#delete the outliers( payments which are less than 1500)
dci_data<-dci_data %>% 
  filter(`payment`>1500) %>% 
  arrange(desc(payment))

#combine 2 separate part_b payments to one
dci_data<-dci_data %>% 
  mutate(part_b = part_b_phys + part_b_dme)

dci_data %>% 
  select(payment,part_a,part_b_phys,part_b_dme,part_b)

#sum(is.na(dci_data$claim_last_dialysis_date))
sum(is.na(dci_data))

[1] 220

str(dci_data)

Classes ‘spec_tbl_df’, ‘tbl_df’, ‘tbl’ and 'data.frame':    9970 obs. of  72 variables:
 $ patient_id               : Factor w/ 672 levels "30111","39732",..: 293 50 64 251 21 31 293 86 441 172 ...
 $ dos_year                 : Factor w/ 2 levels "2017","2018": 1 1 2 1 2 2 1 2 2 2 ...
 $ dos_month                : Factor w/ 12 levels "1","2","3","4",..: 7 7 4 7 3 7 9 2 3 8 ...
 $ claim_first_dialysis_date: Date, format: NA NA NA "2017-07-13" ...
 $ claim_last_dialysis_date : Date, format: NA NA NA "2017-07-29" ...
 $ payment                  : num  128637 122674 121244 112103 109108 ...
 $ part_a                   : num  119013 119153 111345 110036 102586 ...
 $ part_b_phys              : num  9623 3521 9899 2067 6521 ...
 $ part_b_dme               : num  0 0 0 0 0 ...
 $ inpatient                : num  119013 119153 111345 108511 102586 ...
 $ outpatient_dialysis      : num  0 0 0 1525 0 ...
 $ outpatient_er            : num  0 0 0 0 0 ...
 $ outpatient_other         : num  0 0 0 0 0 ...
 $ snf                      : num  0 0 0 0 0 ...
 $ vasc_access              : num  179 0 309 0 0 ...
 $ location_id              : Factor w/ 25 levels "000026","000055",..: 18 22 17 24 25 3 18 1 4 5 ...
 $ modality                 : Factor w/ 3 levels "HH","HIC","PD": 2 2 2 2 2 2 2 2 2 2 ...
 $ epo_protocol_flag        : logi  TRUE TRUE TRUE TRUE TRUE TRUE ...
 $ iron_protocol_flag       : logi  FALSE TRUE TRUE TRUE TRUE TRUE ...
 $ nutsup_protocol_flag     : logi  TRUE TRUE TRUE TRUE TRUE TRUE ...
 $ sup_name                 : Factor w/ 11 levels "Body Quest Ice Cream",..: 4 5 9 4 4 11 4 11 11 1 ...
 $ tx                       : Factor w/ 24 levels "0","1","2","3",..: 9 1 5 1 1 3 1 1 6 1 ...
 $ tx_epo                   : Factor w/ 16 levels "0","1","10","11",..: 9 1 2 1 1 8 1 1 2 1 ...
 $ venofer_given            : logi  FALSE FALSE FALSE FALSE FALSE FALSE ...
 $ ferrlecit_given          : logi  TRUE FALSE FALSE FALSE FALSE FALSE ...
 $ feraheme_given           : logi  FALSE FALSE FALSE FALSE FALSE FALSE ...
 $ zemplar_iv_given         : logi  FALSE FALSE FALSE FALSE FALSE FALSE ...
 $ hectorol_iv_given        : logi  TRUE FALSE TRUE FALSE FALSE TRUE ...
 $ calcijex_iv_given        : logi  FALSE FALSE FALSE FALSE FALSE FALSE ...
 $ zemplar_or_given         : logi  FALSE FALSE FALSE FALSE FALSE FALSE ...
 $ hectorol_or_given        : logi  FALSE FALSE FALSE FALSE FALSE FALSE ...
 $ calcijex_or_given        : logi  FALSE FALSE FALSE FALSE FALSE FALSE ...
 $ activase_given           : logi  FALSE FALSE FALSE FALSE FALSE FALSE ...
 $ prostat_given            : logi  TRUE TRUE TRUE TRUE TRUE TRUE ...
 $ nepro_given              : logi  TRUE TRUE TRUE TRUE TRUE TRUE ...
 $ liquacel_given           : logi  TRUE TRUE TRUE TRUE TRUE TRUE ...
 $ hgb                      : num  11.1 0 8.6 0 0 10.4 0 0 0 0 ...
 $ tsat                     : num  23 39 38 23 32 56 0 0 18 50 ...
 $ ferr                     : num  2051 1401 1591 1037 667 ...
 $ albumin                  : num  4 0 0 0 0 3.5 0 0 0 0 ...
 $ pth                      : num  559 577 224 0 641 ...
 $ ca                       : num  8.7 0 0 0 0 8.7 0 0 0 0 ...
 $ cca                      : num  8.7 0 0 0 0 9.1 0 0 0 0 ...
 $ ph                       : num  4.2 0 0 0 0 5 0 0 0 0 ...
 $ k                        : num  4.9 0 0 0 0 5.1 0 0 0 0 ...
 $ urr                      : num  72 0 70 0 0 78 0 0 0 0 ...
 $ ktv                      : num  1.45 0 1.35 0 0 1.72 0 0 0 0 ...
 $ has_catheter             : logi  TRUE TRUE TRUE TRUE TRUE TRUE ...
 $ aranesp_given            : logi  TRUE TRUE TRUE TRUE TRUE TRUE ...
 $ protinex_given           : logi  TRUE TRUE TRUE TRUE TRUE TRUE ...
 $ hgb_exclude_flag         : logi  FALSE FALSE FALSE FALSE FALSE FALSE ...
 $ mircera_given            : logi  FALSE FALSE FALSE FALSE FALSE FALSE ...
 $ sensipar_given           : logi  FALSE FALSE TRUE FALSE TRUE TRUE ...
 $ parsabiv_given           : logi  FALSE FALSE TRUE FALSE TRUE TRUE ...
 $ protein_bar_given        : logi  FALSE FALSE FALSE FALSE FALSE TRUE ...
 $ ice_cream_given          : logi  FALSE FALSE FALSE FALSE FALSE TRUE ...
 $ gelatein_given           : logi  FALSE FALSE FALSE FALSE FALSE TRUE ...
 $ active_flag              : logi  TRUE TRUE TRUE TRUE TRUE TRUE ...
 $ hospital_episodes        : num  2 1 2 2 1 3 1 0 1 1 ...
 $ hospital_admits          : num  2 0 2 1 0 3 0 0 1 0 ...
 $ hospital_discharges      : num  1 0 1 1 0 3 0 0 0 0 ...
 $ er_visits                : num  0 0 0 0 0 0 0 0 1 0 ...
 $ snf_episodes             : num  0 0 0 0 0 1 0 1 1 0 ...
 $ snf_admit                : num  0 0 0 0 0 0 0 0 0 0 ...
 $ snf_discharge            : num  0 0 0 0 0 1 0 0 1 0 ...
 $ med_orders               : num  8 16 10 7 12 46 8 9 20 16 ...
 $ cardio                   : num  2 5 2 2 3 8 2 2 1 3 ...
 $ beta_blockers            : num  1 1 0 1 1 1 1 1 1 1 ...
 $ antihypertensives        : num  0 1 0 0 0 3 0 0 0 0 ...
 $ opioids                  : num  1 0 0 0 0 1 1 1 1 0 ...
 $ antidiabetics            : num  0 1 3 1 1 4 0 0 1 3 ...
 $ part_b                   : num  9623 3521 9899 2067 6521 ...

copy the cleaned dataset into another dataset just as a back up

dci_data_ws<- dci_data
saveRDS(dci_data_ws,file="dci_data.rds")

Histograms to understand the distribution of the data for payments

dci_data_ws %>% 
  select (payment,part_a,part_b_phys,part_b_phys) %>% 
  ggplot(aes(x=payment) )+
  geom_histogram(color="#e9ecef", alpha=0.6, position = 'identity', bins=40) +
  scale_x_log10()+
scale_fill_manual(values=c("#69b3a2", "#404080")) +
 labs(x="loggged payment values",
      y="Counts",
      title = "Payment distribution")

#make this interactive
p<- ggplot(dci_data_ws,aes(x=payment,y=..density..)) +
  geom_histogram(bins=40,binwidth = 0.05,fill = "black",color="black",alpha=0.2) +
  scale_x_log10()+
geom_density(color = "red")+
 labs(x="Logged payments",
      y="Frequency",
      title = "Total Payments distribution")
ggplotly(p)

NA
NA
NA

#make this interactive
p<- ggplot(dci_data_ws,aes(x=payment,fill=modality)) +
  geom_histogram(bins=10,binwidth = 0.25,alpha=0.8,position = "dodge") +
  scale_x_log10()+
 labs(x="loggged payment values",
      y="Frequency",
      title = "Payment distribution")
p

Modality distributions

#density distribution for modalities
ggplot(dci_data_ws,aes(payment, color = modality,fill=modality)) +
  scale_x_log10()+
  geom_density(alpha = 0.8,position = "dodge")

dci_data_ws %>% 
  select(payment,modality) %>% 
  group_by(modality) %>% 
  summarise(total_payments=sum(payment),num_of_rows=n())

#Function to create an histgram for each kind of madality HIC,HH AND PD
make_plot <- function(mod){
  dci_data_ws %>% 
  select(payment,modality) %>% 
  filter(modality == mod) %>% 
   ggplot(aes(x=payment)) +
  geom_histogram(binwith=1000) +
  scale_x_log10() +
  labs(x="payments",
       y="frequency",
       title = paste("Distribution of",mod))
}
make_plot('HH')

Ignoring unknown parameters: binwith

make_plot('HIC')

Ignoring unknown parameters: binwith

make_plot('PD')

Ignoring unknown parameters: binwith

dci_data_ws %>% 
  select(payment,modality) %>% 
  filter(modality == 'HH') %>% 
   ggplot(aes(x=payment)) +
  geom_histogram() +
  labs(x="payments",
       y="frequency",
       title = "Distribution of Hemo Home Payments")

NA

dci_data_ws %>% 
select(payment,modality) %>% 
#filter(payment>2800 & payment <3500) %>% 
  group_by(modality) 

NA
NA
NA
NA

dci_data_ws %>% 
  select(payment,modality) %>% 
  filter(payment>2800 & payment <3500) %>% 
  #group_by(modality) %>% 
  filter(modality=='PD') %>% 
  ggplot(aes(x=payment)) +
  geom_histogram(binwidth = 4000)

dci_data_ws %>% 
  select(payment,modality) %>% 
  filter(payment>2800 & payment <3500) 

library(reshape2)
dci_long <- reshape2::melt(dci_data_ws)

Using patient_id, dos_year, dos_month, location_id, modality, epo_protocol_flag, iron_protocol_flag, nutsup_protocol_flag, sup_name, tx, tx_epo, venofer_given, ferrlecit_given, feraheme_given, zemplar_iv_given, hectorol_iv_given, calcijex_iv_given, zemplar_or_given, hectorol_or_given, calcijex_or_given, activase_given, prostat_given, nepro_given, liquacel_given, has_catheter, aranesp_given, protinex_given, hgb_exclude_flag, mircera_given, sensipar_given, parsabiv_given, protein_bar_given, ice_cream_given, gelatein_given, active_flag as id variables
attributes are not identical across measure variables; they will be dropped

ggplot(dci_long, aes(value)) + facet_wrap(~variable, scales = 'free_x') +
  geom_histogram()

NA
NA

#frequency distribution with density lines
#x<- sample(0:30, 200, replace=T, prob=15 - abs(15 - 0:30))
x<-dci_data_ws$payment
## Calculate and plot the two histograms
hcum <- h <- hist(x, plot=FALSE)
hcum$counts <- cumsum(hcum$counts)
plot(hcum, main="")
plot(h, add=T, col="grey")

## Plot the density and cumulative density
d <- density(x)
lines(x = d$x, y = d$y * length(x) * diff(h$breaks)[1], lwd = 2)
lines(x = d$x, y = cumsum(d$y)/max(cumsum(d$y)) * length(x), lwd = 2)

#distribution by payment type
 payment_type_dist<-dci_data_ws %>% 
                      select(dos_year,dos_month,payment,part_a,part_b_dme,part_b_phys,part_b)%>% 
                       pivot_longer(payment:part_b,names_to="payment_type",values_to = "payment") 

saveRDS(payment_type_dist,file="payment_type_dist.rds")

#plot the fequency distribution across different payment types
  
  pl<-dci_data_ws %>% 
        select(dos_year,dos_month,location_id,payment,part_a,part_b_dme,part_b_phys,part_b)%>% 
        pivot_longer(payment:part_b,names_to="payment_type",values_to = "payment") %>% 
        filter(dos_year==2017 & dos_month==1) %>% 
        ggplot( aes(x=payment,fill=payment_type,color= payment_type)) +
        geom_histogram(bins= 40,binwidth = 0.05,alpha=0.3,position = "identity") +
        scale_x_log10() +
  geom_vline(aes(xintercept = mean(payment,na.rm=T)),color = "red", linetype = "dashed",size = 1) +
  labs(x="Logged payments",y="Frequency",title = "distribution of payments across payment sources") 
        
ggplotly(pl)

Transformation introduced infinite values in continuous x-axisRemoved 286 rows containing non-finite values (stat_bin).

sum(is.na(dci_data$payment))

[1] 0

#fat tailed dis. 
box_pl<-payment_type_dist %>% 
  filter(dos_year == 2017 & dos_month == 1) %>% 
  ggplot(aes(y = payment,x = payment_type,fill=payment_type)) +
  geom_boxplot()+
  #  scale_y_log10() +
  theme_classic() +
  labs(x="Payment type", y = "Payment",
       title = "Payment distribution by payment types")
 
ggplotly(box_pl)

years_plot<- dci_data_ws %>% 
  group_by(dos_year,dos_month) %>% 
  summarise(sum_pay = sum(payment)) %>% 
  ungroup()  

pl <- ggplot( years_plot,aes(y=sum_pay, x = dos_month ,fill=dos_year)) +
  geom_bar(stat = "identity",position = 'dodge') +
  labs(x="Months", y= "Payments") +
  ggtitle("Total payments increase in each month")
       
pl

years_plot

NA

dci_data_ws %>% 
  group_by(modality) %>% 
  summarise(payments = sum(payment)) %>%  
  ggplot(aes(x=modality,y=payments,fill = `payments`)) +
  geom_bar(stat="identity" ) 

NA
NA

#show how modalities are doing by total payments 
modality_pl<-dci_data_ws %>% 
  group_by(dos_year,modality) %>% 
  summarise(payments = sum(payment)) %>% 
ungroup() %>% 
  ggplot(aes(x=dos_year,y=payments,fill=modality,color=modality))+
  geom_bar(stat="identity",position="dodge") +
   geom_text(aes(label = round(payments),vjust=0),postion=position_dodge(width=5)) +
labs(x="Modalities in 2017 and 2018",y="payments",title="Payments by modality")

Ignoring unknown parameters: postion

modality_pl

dci_data_ws  %>% 
  ggplot(aes(x=dos_year,y=payment,color = modality)) +
    geom_boxplot() +
  scale_y_log10() +
  labs(x = "Years",y="payments", 
       title = "Payments in in 2017 and 2018")

correlational plot

num_columns <- c("hgb","tsat","ferr","albumin","pth","ca","cca","ph","k","urr","ktv")
corrs<-dci_data_ws %>% select(num_columns) %>% 
 # drop_na_() %>% 
  cor()
corrs

               hgb          tsat        ferr    albumin          pth         ca        cca            ph         k        urr
hgb     1.00000000  0.0527380941  0.02570081 0.59628329  0.045803409 0.58340275 0.56441343  0.3009506342 0.4724638 0.21430294
tsat    0.05273809  1.0000000000  0.39986167 0.08686222 -0.027163707 0.04433107 0.03910477  0.0002180982 0.0788412 0.06797837
ferr    0.02570081  0.3998616659  1.00000000 0.05132422  0.005239110 0.05485813 0.05667298 -0.0513212701 0.1183593 0.20079170
albumin 0.59628329  0.0868622245  0.05132422 1.00000000  0.104443153 0.76304243 0.67544496  0.3994618231 0.6250713 0.31241463
pth     0.04580341 -0.0271637069  0.00523911 0.10444315  1.000000000 0.03478453 0.01573087  0.2973311051 0.1093904 0.02051048
ca      0.58340275  0.0443310656  0.05485813 0.76304243  0.034784525 1.00000000 0.98442825  0.3359978827 0.6256086 0.27040412
cca     0.56441343  0.0391047722  0.05667298 0.67544496  0.015730870 0.98442825 1.00000000  0.3196562589 0.6140480 0.25157942
ph      0.30095063  0.0002180982 -0.05132127 0.39946182  0.297331105 0.33599788 0.31965626  1.0000000000 0.4446666 0.08193608
k       0.47246376  0.0788412002  0.11835935 0.62507130  0.109390410 0.62560864 0.61404796  0.4446665609 1.0000000 0.34881524
urr     0.21430294  0.0679783689  0.20079170 0.31241463  0.020510483 0.27040412 0.25157942  0.0819360755 0.3488152 1.00000000
ktv     0.24719233  0.0879913547  0.06463618 0.27081825  0.004094452 0.30377824 0.30664367  0.0767076454 0.2096817 0.08050080
                ktv
hgb     0.247192331
tsat    0.087991355
ferr    0.064636183
albumin 0.270818252
pth     0.004094452
ca      0.303778243
cca     0.306643666
ph      0.076707645
k       0.209681668
urr     0.080500799
ktv     1.000000000

library(corrplot)
corrplot(corrs,type = "upper",order = "hclust",
         tl.col="black",tl.srt=45)

num_columns <- c("payment","hgb","tsat","ferr","albumin","pth","ca","cca","ph","k","urr","ktv")
corrs<-dci_data_ws %>% select(num_columns)  %>% 
cor()


corrplot(corrs,type = "upper",order = "hclust",
         tl.col="black",tl.srt=45)

NA
NA

tibble('variable' = corrs[1,2:12] %>% names(),'correlation' = corrs[1,2:12]) %>% 
  ggplot(aes(x=reorder(variable,correlation),y = correlation)) +
  geom_point()+
  geom_segment(aes(xend=variable,yend=0))+
  coord_flip() +
  geom_hline(yintercept = 0)

pl <- ggplot(dci_data_ws, aes(x=albumin,y=payment) ) +
  geom_point(alpha=0.2) + geom_smooth(method = 'lm') +
  scale_x_log10() +
  scale_y_log10()

ggplotly(pl)

Transformation introduced infinite values in continuous x-axisTransformation introduced infinite values in continuous x-axisRemoved 195 rows containing non-finite values (stat_smooth).

#unique(dci_data_ws$ktv)
#hgb vs payment

ggplot(dci_data_ws, aes(x=hgb,y=payment) ) +
  geom_point() + geom_smooth(method = 'lm') +
  scale_x_log10() +
  labs(x="hgb", y = "payment", title = "Payment Vs hgb")

NA
NA

ggplot(dci_data_ws, aes(x=ca,y=payment) ) +
  geom_point() + geom_smooth(method = 'lm') +
  scale_x_log10() +
  labs(x="ca", y = "payment", title = "Payment Vs ca")

Maps

#first prep the dataset to get the information on the map markers

 payment_summary_by_loc<-dci_data_ws %>% 
  select(patient_id,location_id,dos_month,dos_year,payment,part_a,part_b_phys,part_b_dme,modality) %>% 
  group_by(location_id,dos_month,dos_year ) %>% 
  summarise(
            total_patients = NROW(patient_id),
            totalpayments = round(sum(payment),digits=2),
            min_pay = min(payment),
            max_pay = max(payment),
            avg_pay = sum(payment)/NROW(patient_id)) %>% 
  ungroup() %>%
  arrange(dos_year,dos_month)  

#merge the grouped data with location dataset
payment_geom_summary <- merge(payment_summary_by_loc,locations,by = "location_id")

payment_geom_summary %>%
  filter(location_id =='000055' & dos_month ==1 & dos_year ==2017)

  
#save it to rds file
saveRDS(payment_geom_summary, file = "DCI_midcourse/data/payment_geom_summary.rds")

 

payment_geom_summary %>% 
  arrange(desc(totalpayments)) %>% 
  filter(dos_year==2017 & dos_month == 1)

NA

#Top 5 locations with high average payments

loc_plt<-payment_summary_by_loc %>% 
  arrange(desc(totalpayments)) %>% 
  filter(dos_year==2017 & dos_month == 4) %>% 
  top_n(5) %>% 
  ggplot(aes(x=location_id,y=avg_pay)) +
  geom_col( )

Selecting by avg_pay

ggplotly(loc_plt)

NA
NA

leaflet(data= locations) %>% 
  addTiles() %>% 
  addMarkers(~longitude,
             ~latitude,
             popup = ("hello"))

pl <- ggplot(dci_data_ws, aes(x=albumin,y=payment) ) +
  geom_point(alpha=0.2) + geom_smooth(method = 'lm') +
  scale_x_log10() +
  scale_y_log10()

ggplotly(pl)

Transformation introduced infinite values in continuous x-axisTransformation introduced infinite values in continuous x-axisRemoved 195 rows containing non-finite values (stat_smooth).

dci_data_ws %>% 
  select(location_id,dos_year,dos_month,payment,part_a,part_b) %>% 
  mutate(percentage_a = round(part_a/payment*100),
         percentage_b = round(part_b/payment*100))

NA
NA
NA

dci_data_ws %>% 
  select(dos_year,dos_month,payment,part_a,part_b) %>% 
  mutate(percent_part_a = round(part_a/payment*100)) %>% 
  filter(dos_year=2017,dos_month=1)

dci_data_shiny %>% 
  select (location_id,patient_id,payment,part_a,part_b)

NA

install.packages("sunburstR")

also installing the dependency ‘d3r’

trying URL 'https://cran.rstudio.com/bin/macosx/el-capitan/contrib/3.6/d3r_0.8.7.tgz'
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downloaded 395 KB

trying URL 'https://cran.rstudio.com/bin/macosx/el-capitan/contrib/3.6/sunburstR_2.1.3.tgz'
Content type 'application/x-gzip' length 532381 bytes (519 KB)
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downloaded 519 KB

The downloaded binary packages are in
    /var/folders/v7/gmk316lj6f7_cdbjbr0vvjgw0000gn/T//Rtmplevi5y/downloaded_packages